Controlling chaotic discrete dynamical systems through fixed point iterative methods

A simple and natural technique for controlling chaos in discrete dynamical systems, originating in iterative approximation of fixed points, is developed. We show by numerical simulations that our technique of stabilizing unstable periodic points of chaotic discrete systems is effective and, moreover, compared to other stabilizing methods, has a extremely high speed. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Controlling chaotic behaviour for spin generator and Rossler dynamical systems with feedback control

Different methods are proposed to control chaotic behaviour of the Nuclear Spin Generator (NSG) and Rossler continuous dynamical systems. Linear and nonlinear feedback control techniques are used to suppress chaos. The stabilization of unstable fixed point or unstable periodic solution of chaotic behaviour is achieved. The controlled system is stable under some conditions on the parameters of the system. Stability of the controlled system is determined by the Routh–Hurwitz criterion and Lyapunov direct method. Numerical simulation results are included to show the control process.     

LDG method for reaction-diffusion dynamical systems with time delay

In this paper we introduce a local discontinuous Galerkin method to solve nonlinear reaction-diffusion dynamical systems with time delay. Stability and convergence of the schemes are obtained. Finally, numerical examples on two biologic models are shown to demonstrate the accuracy and stability of the method.     

A method for designing dynamical S-boxes based on discretized chaotic map

A method for obtaining dynamically cryptographically strong substitution boxes (S-boxes) based on discretized chaotic map is presented in this paper. The cryptographical properties such as bijection, nonlinearity, strict avalanche, output bits independence and equiprobable input/output XOR distribution of these S-boxes are analyzed in detail. The results of numerical analysis show that all the criteria for designing good S-box can be met approximately. As a result, our approach is suitable for practical application in designing block cryptosystem.     

基于分数阶混沌时间序列的图像加密算法

基于分数阶logistic映射提出了洗牌加密方法.通过离散分数阶微积分得到分数阶序列并把它作为密钥.利用位异或算子,提出了一种新的图像加密算法.对该算法的密钥空间、密钥敏感性和统计特性进行相应的仿真分析.结果表明,该算法可以达到较好的加解密效果,具有很高的安全性,可以满足图像加密安全性的要求.     

On chaotic extensions of dynamical systems

In this paper, inspired by some results in linear dynamics, we will show that every dynamical system (X,f), where f is a continuous self-map on a separable metric space X, can be extended to a chaotic (in the sense of Devaney) dynamical system in an isometric way.     

On chaotic set-valued discrete dynamical systems

Let (X, d) be a metric space and let f: (X, d) → (X, d) be a continuous map. In this note we investigate the relationships between the chaoticity of some set-valued discrete dynamical systems associated to f (collective chaos) and the chaoticity of f (individual chaos).     

Limit theorems and Markov approximations for chaotic dynamical systems

Summary We prove the central limit theorem and weak invariance principle for abstract dynamical systems based on bounds on their mixing coefficients. We also develop techniques of Markov approximations for dynamical systems. We apply our results to expanding interval maps, Axiom A diffeomorphisms, chaotic billiards and hyperbolic attractors.     

Model based method for estimating an attractor dimension from uni/multivariate chaotic time series with application to Bremen climatic dynamics

In this paper, a method for estimating an attractor embedding dimension based on polynomial models and its application in investigating the dimension of Bremen climatic dynamics are presented. The attractor embedding dimension provides the primary knowledge for analyzing the invariant characteristics of the attractor and determines the number of necessary variables to model the dynamics. Therefore, the optimality of this dimension has an important role in computational efforts, analysis of the Lyapunov exponents, and efficiency of modeling and prediction. The smoothness property of the reconstructed map implies that, there is no self-intersection in the reconstructed attractor. The method of this paper relies on testing this property by locally fitting a general polynomial autoregressive model to the given data and evaluating the normalized one step ahead prediction error. The corresponding algorithms are developed in uni/multivariate form and some probable advantages of using information from other time series are discussed. The effectiveness of the proposed method is shown by simulation results of its application to some well-known chaotic benchmark systems. Finally, the proposed methodology is applied to two major dynamic components of the climate data of the Bremen city to estimate the related minimum attractor embedding dimension.     

Differential phase space reconstructed for chaotic time series

A new numerical differential filter is built to estimate the numerical differential for a chaotic time series and then a differential phase space for the chaotic time series is reconstructed. Correlation dimensions, Lyapunov exponents and forecasting are discussed for the chaotic time series on the reconstructed differential phase space and on the delay phase space, respectively. Comparison results show that the numerical results on the differential phase space are better than that on the delay phase space.     

Stochastic stability in some chaotic dynamical systems

For a certain class of piecewise monotonic transformations it is shown using a spectral decomposition of the Perron-Frobenius-operator ofT that invariant measures depend continuously on 3 types of perturbations: 1) deterministic perturbations, 2) stochastic perturbations, 3) randomly occuring deterministic perturbations. The topology on the space of perturbed transformations is derived from a metric on the space of Perron-Frobenius-operators.With 1 Figure     

A directed weighted complex network for characterizing chaotic dynamics from time series

We propose a reliable method for constructing a directed weighted complex network (DWCN) from a time series. Through investigating the DWCN for various time series, we find that time series with different dynamics exhibit distinct topological properties. We indicate this topological distinction results from the hierarchy of unstable periodic orbits embedded in the chaotic attractor. Furthermore, we associate different aspects of dynamics with the topological indices of the DWCN, and illustrate how the DWCN can be exploited to detect unstable periodic orbits of different periods. Examples using time series from classical chaotic systems are provided to demonstrate the effectiveness of our approach.     

Taylor series method for dynamical systems with control: Convergence and error estimates

To optimize a complicated function constructed from a solution of a system of ordinary differential equations (ODEs), it is very important to be able to approximate a solution of a system of ODEs very precisely. The precision delivered by the standard Runge-Kutta methods often is insufficient, resulting in a “noisy function” to optimize. We consider an initial-value problem for a system of ordinary differential equations having polynomial right-hand sides with respect to all dependent variables. First we show how to reduce a wide class of ODEs to such polynomial systems. Using the estimates for the Taylor series method, we construct a new “aggregative” Taylor series method and derive guaranteed a priori step-size and error estimates for Runge-Kutta methods of order r. Then we compare the 8,13-Prince-Dormand’s, Taylor series, and aggregative Taylor series methods using seven benchmark systems of equations, including van der Pol’s equations, the “brusselator,” equations of Jacobi’s elliptic functions, and linear and nonlinear stiff systems of equations. The numerical experiments show that the Taylor series method achieves the best precision, while the aggregative Taylor series method achieves the best computational time. The final section of this paper is devoted to a comparative study of the above numerical integration methods for systems of ODEs describing the optimal flight of a spacecraft from the Earth to the Moon. __________ Translated from Sovremennaya Matematika i Ee Prilozheniya (Contemporary Mathematics and Its Applications), Vol. 24, Dynamical Systems and Optimization, 2005.     

Recursive prediction of chaotic time series

Summary Considerable progress has been made in recent years in the analysis of time series arising from chaotic systems. In particular, a variety of schemes for the short-term prediction of such time series has been developed. However, hitherto all such algorithms have used batch processing and have not been able to continuously update their estimate of the dynamics using new observations as they are made. This severely limits their usefulness in real time signal processing applications. In this paper we present a continuous update prediction scheme for chaotic time series that overcomes this difficulty. It is based on radial basis function approximation combined with a recursive least squares estimation algorithm. We test this scheme using simulated data and comment on its relationship to adaptive transversal filters, which are widely used in conventional signal processing.     

On Taylor series expansion for chaotic nonlinear systems

We study properties of Taylor series expansion for maps displaying chaotic behaviour. Analytical and numerical results are presented to illustrate that under certain conditions the trajectory of the map obtained by the expansion may not represent the original trajectory, even if the Taylor series converges.